def __init__(self,
instruments,
scripts,
name=None,
settings=None,
log_function=None,
data_path=None):
"""
Standard script initialization
Args:
name (optional): name of script, if empty same as class name
settings (optional): settings for this script, if empty same as default settings
"""
self._DEFAULT_SETTINGS += PulseBlasterBaseScript._DEFAULT_SETTINGS
Script.__init__(self,
name,
settings=settings,
scripts=scripts,
instruments=instruments,
log_function=log_function,
data_path=data_path)
def visualize_magnetic_fields(folder,
manual=True,
legend_location='upper right',
bbox_to_anchor=(1, 1),
scatter_plot_axis='x',
line_points=None):
"""
Creates, plots, and saves two plots of NV data. The first is a plot of the fluoresence image, with a tag on each NV
indicating whether it is split or unsplit. The second is a plot of the magnitude of the splitting and associated
magnetic field vs the NV x coordinate.
Args:
folder: folder of original data
manual: True if manual processing has been done first, such as by process_esrs in
b26_toolkit.src.data_analysis.esr_post_processing. False if only the raw data should be used.
legend_location: location of legend in plot, using standard matplotlib location tags. Use this argument to move
the legend if it covers the NVs.
"""
# load the fit_data
if manual:
df = pd.read_csv(os.path.join('./==processed==',
'{:s}/data-manual.csv'.format(folder)),
index_col='id',
skipinitialspace=True)
else:
df = pd.read_csv(os.path.join(
folder, '{:s}.csv'.format(folder.split('./')[1].replace('/',
'-'))),
index_col='id',
skipinitialspace=True)
df = df.drop('Unnamed: 0', 1)
# include manual corrections
if manual:
for id, nv_type in enumerate(df['manual_nv_type']):
if not pd.isnull(nv_type):
df.set_value(id, 'NV type', nv_type)
if nv_type == 'split':
df.set_value(id, 'B-field (gauss)',
df.get_value(id, 'manual_B_field'))
# load the image data
select_points_data = Script.load_data(get_select_points(folder))
image_data = select_points_data['image_data']
# points_data = select_points_data['nv_locations']
extent = select_points_data['extent']
pt1x = widgets.FloatText(description='upper point x')
pt1y = widgets.FloatText(description='upper point y')
pt2x = widgets.FloatText(description='lower point x')
pt2y = widgets.FloatText(description='lower point y')
display(pt1x)
display(pt1y)
display(pt2x)
display(pt2y)
# prepare figure
f = plt.figure(figsize=(15, 8))
gs = gridspec.GridSpec(1, 2, height_ratios=[1, 1])
ax0 = plt.subplot(gs[0])
ax1 = plt.subplot(gs[1])
def onclick(event):
if event.button == 1:
pt1x.value = event.xdata
pt1y.value = event.ydata
elif event.button == 3:
pt2x.value = event.xdata
pt2y.value = event.ydata
cid = f.canvas.mpl_connect('button_press_event', onclick)
# plot the map
ax0.imshow(image_data, extent=extent, interpolation='nearest', cmap='pink')
for nv_type, color in zip(['split', 'bad', 'no_peak', 'single'],
['g', 'k', 'b', 'r']):
subset = df[df['NV type'] == nv_type]
ax0.scatter(subset['xo'], subset['yo'], c=color, label=nv_type)
ax0.legend(loc=legend_location, bbox_to_anchor=bbox_to_anchor)
subset = df[df['NV type'] == 'split']
for i, j, n in zip(subset['xo'], subset['yo'], subset.index):
corr = +0.005 # adds a little correction to put annotation in marker's centrum
ax0.annotate(str(n), xy=(i + corr, j + corr), fontsize=8, color='w')
ax0.set_xlabel('x (V)')
ax0.set_ylabel('y (V)')
# plot the fields on a 1D plot
subset = df[df['NV type'] == 'split']
if scatter_plot_axis == 'x':
x_coor = subset['xo']
elif scatter_plot_axis == 'y':
x_coor = subset['yo']
elif scatter_plot_axis == 'line':
pt1 = line_points[0]
pt2 = line_points[1]
x_coor = []
for x, y in zip(subset['xo'], subset['yo']):
x_coor.append(distance_point_to_line([x, y], pt1, pt2))
ax1.plot(x_coor, subset['B-field (gauss)'] / freq_to_mag * 1e-6, 'go')
for i, j, n in zip(x_coor, subset['B-field (gauss)'] / freq_to_mag * 1e-6,
subset.index):
corr = 0.0005 # adds a little correction to put annotation in marker's centrum
ax1.annotate(str(n), xy=(i + corr, j + corr))
subset = df[df['NV type'] == 'single']
for i, j, n in zip(subset['xo'], subset['yo'], subset.index):
corr = +0.005 # adds a little correction to put annotation in marker's centrum
ax0.annotate(str(n), xy=(i + corr, j + corr), fontsize=8, color='w')
subset = df[df['NV type'] == 'single']
subset_dict = {'xo': [], 'yo': [], 'B-field (gauss)': [], 'index': []}
for nv in subset.iterrows():
if 'fit_4' in nv[1]:
subset_dict['xo'].append(nv[1][9])
subset_dict['yo'].append(nv[1][11])
else:
subset_dict['xo'].append(nv[1][7])
subset_dict['yo'].append(nv[1][9])
subset_dict['index'].append(nv[0])
if np.isnan(nv[1][6]) and ((nv[1][4]) > 2.92e9 or (nv[1][4]) < 2.82e9):
subset_dict['B-field (gauss)'].append(
(nv[1][4] - 2.87e9) * 2 * freq_to_mag)
else:
subset_dict['B-field (gauss)'].append(0)
if scatter_plot_axis == 'x':
print('HERE')
x_coor = subset_dict['xo']
elif scatter_plot_axis == 'y':
x_coor = subset_dict['yo']
elif scatter_plot_axis == 'line':
pt1 = line_points[0]
pt2 = line_points[1]
x_coor = []
for x, y in zip(subset_dict['xo'], subset_dict['yo']):
x_coor.append(distance_point_to_line([x, y], pt1, pt2))
ax1.plot(x_coor,
np.array(subset_dict['B-field (gauss)']) / freq_to_mag * 1e-6,
'ro')
for i, j, n in zip(
x_coor,
np.array(subset_dict['B-field (gauss)']) / freq_to_mag * 1e-6,
subset_dict['index']):
corr = 0.0005 # adds a little correction to put annotation in marker's centrum
ax1.annotate(str(n), xy=(i + corr, j + corr))
ax1.set_title('ESR Splittings')
if scatter_plot_axis == 'x':
ax1.set_xlabel('x coordinate (V)')
if scatter_plot_axis == 'y':
ax1.set_xlabel('y coordinate (V)')
if scatter_plot_axis == 'line':
ax1.set_xlabel('Distance to magnet edge (V)')
ax1.set_ylabel('Splitting (MHz)')
if not scatter_plot_axis == 'line':
ax1.set_xlim([extent[0], extent[1]])
plt.axvline(x=0, color='black', linestyle='dashed', linewidth=2)
ax2 = ax1.twinx()
mn, mx = ax1.get_ylim()
ax2.set_ylim(mn * freq_to_mag * 1e6, mx * freq_to_mag * 1e6)
ax2.set_ylabel('Magnetic Field Projection (Gauss)')
# f.set_tight_layout(True)
print(
'path',
os.path.join('./==processed==', folder[2:],
'splittings_plot.jpg'.format(os.path.basename(folder))))
f.savefig(os.path.join(
'./==processed==', folder[2:],
'splittings_plot.jpg'.format(os.path.basename(folder))),
bbox_inches='tight',
pad_inches=0)
if not delete_list == []:
delete_list.reverse()
for index in delete_list:
pulse_sequences.pop(index)
# print('delete taus from list:')
# print(delete_list)
tau_list = np.delete(np.array(tau_list), delete_list).tolist()
return pulse_sequences, num_averages, tau_list, measurement_gate_width
def stop(self):
"""
Stop currently executed pulse blaster sequence
NOT CURRENTLY WORKING, WILL CRASH PULSEBLASTER
"""
# self.instruments['PB']['instance'].stop()
super(PulseBlasterBaseScript, self).stop()
if __name__ == '__main__':
script = {}
instr = {}
script, failed, instr = Script.load_and_append(
{'ExecutePulseBlasterSequence': 'ExecutePulseBlasterSequence'}, script,
instr)
print(script)
print(failed)
print(instr)
def perform_affine_transform(img_path_old, img_path_new, done_queue,
return_queue):
import time
pt11_widget = widgets.HTML("Pt 1: 0,0")
pt12_widget = widgets.HTML("Pt 2: 0,0")
pt13_widget = widgets.HTML("Pt 3: 0,0")
pt21_widget = widgets.HTML("Pt 1: 0,0")
pt22_widget = widgets.HTML("Pt 2: 0,0")
pt23_widget = widgets.HTML("Pt 3: 0,0")
display(pt11_widget)
display(pt12_widget)
display(pt13_widget)
display(pt21_widget)
display(pt22_widget)
display(pt23_widget)
# f = plt.figure(figsize=(12, 6))
# ax0 = plt.subplot(121)
# ax1 = plt.subplot(122)
f = plt.figure(figsize=(16, 8))
gs = gridspec.GridSpec(1, 2, width_ratios=[1, 2])
ax0 = plt.subplot(gs[0])
ax1 = plt.subplot(gs[1])
def onclick_old(event):
if ax0.in_axes(event):
if event.key == '1':
pt11_widget.value = 'Pt 1: ' + str(event.xdata) + ',' + str(
event.ydata)
elif event.key == '2':
pt12_widget.value = 'Pt 2: ' + str(event.xdata) + ',' + str(
event.ydata)
elif event.key == '3':
pt13_widget.value = 'Pt 3: ' + str(event.xdata) + ',' + str(
event.ydata)
elif ax1.in_axes(event):
if event.key == '1':
pt21_widget.value = 'Pt 1: ' + str(event.xdata) + ',' + str(
event.ydata)
elif event.key == '2':
pt22_widget.value = 'Pt 2: ' + str(event.xdata) + ',' + str(
event.ydata)
elif event.key == '3':
pt23_widget.value = 'Pt 3: ' + str(event.xdata) + ',' + str(
event.ydata)
cid = f.canvas.mpl_connect('button_press_event', onclick_old)
data_im_old = Script.load_data(img_path_old)
image_old = data_im_old['image_data']
extent_old = data_im_old['extent']
nv_pts_old = data_im_old['nv_locations']
plot_fluorescence_new(image_old, extent_old, ax0)
f.get_axes()[2].remove()
data_im_new = Script.load_data(img_path_new)
image_new = data_im_new['image_data']
extent_new = data_im_new['extent']
nv_pts_new = data_im_new['nv_locations']
plot_fluorescence_new(image_new, extent_new, ax1)
f.get_axes()[2].remove()
plt.show()
while done_queue.empty():
time.sleep(.5)
pt11 = map(
float,
pt11_widget.value.replace(" ", "").replace(':', ',').split(',')[1:3])
pt12 = map(
float,
pt12_widget.value.replace(" ", "").replace(':', ',').split(',')[1:3])
pt13 = map(
float,
pt13_widget.value.replace(" ", "").replace(':', ',').split(',')[1:3])
pt21 = map(
float,
pt21_widget.value.replace(" ", "").replace(':', ',').split(',')[1:3])
pt22 = map(
float,
pt22_widget.value.replace(" ", "").replace(':', ',').split(',')[1:3])
pt23 = map(
float,
pt23_widget.value.replace(" ", "").replace(':', ',').split(',')[1:3])
Avec = calc_transform_matrix((pt11, pt12, pt13), (pt21, pt22, pt23))
Amat = [[Avec[0], Avec[1], Avec[2]], [Avec[3], Avec[4], Avec[5]],
[0, 0, 1]]
shifted_nv_pts_old = []
for pt in nv_pts_old:
circ = patches.Circle((pt[0], pt[1]), .0005, fc='k', ec='k')
ax0.add_patch(circ)
vec = [pt[0], pt[1], 1]
vec_prime = np.dot(Amat, vec)
circ = patches.Circle((vec_prime[0], vec_prime[1]),
.0005,
fc='k',
ec='k')
ax1.add_patch(circ)
shifted_nv_pts_old.append([vec_prime[0], vec_prime[1]])
for pt in nv_pts_new:
circ = patches.Circle((pt[0], pt[1]), .0005, fc='g', ec='g')
ax1.add_patch(circ)
tree = scipy.spatial.KDTree(shifted_nv_pts_old)
_, new_to_old_map = tree.query(nv_pts_new, distance_upper_bound=.005)
#kd tree returns value of len(new_to_old_map) if no match found, change this to -1
new_to_old_map = [
x if x != len(nv_pts_old) else -1 for x in new_to_old_map
]
return_queue.put(new_to_old_map)